Neural network interatomic potentials (NNIPs) are an attractive alternative to ab-initio methods for molecular dynamics (MD) simulations. However, they can produce unstable simulations which sample unphysical states, limiting their usefulness for modeling phenomena occurring over longer timescales. To address these challenges, we present Stability-Aware Boltzmann Estimator (StABlE) Training, a multi-modal training procedure which combines conventional supervised training from quantum-mechanical energies and forces with reference system observables, to produce stable and accurate NNIPs. StABlE Training iteratively runs MD simulations to seek out unstable regions, and corrects the instabilities via supervision with a reference observable. The training procedure is enabled by the Boltzmann Estimator, which allows efficient computation of gradients required to train neural networks to system observables, and can detect both global and local instabilities. We demonstrate our methodology across organic molecules, tetrapeptides, and condensed phase systems, along with using three modern NNIP architectures. In all three cases, StABlE-trained models achieve significant improvements in simulation stability and recovery of structural and dynamic observables. In some cases, StABlE-trained models outperform conventional models trained on datasets 50 times larger. As a general framework applicable across NNIP architectures and systems, StABlE Training is a powerful tool for training stable and accurate NNIPs, particularly in the absence of large reference datasets.

翻译：神经网络原子间势能（NNIPs）是分子动力学（MD）模拟中从头算方法的有吸引力替代方案。然而，它们可能产生不稳定的模拟过程，导致采样到非物理状态，限制了其在长时间尺度现象建模中的实用性。为解决这些挑战，我们提出稳定性感知玻尔兹曼估计器（StABlE）训练，这是一种多模态训练流程，将基于量子力学能量和力的传统监督训练与参考系统可观测量相结合，以生成稳定且精确的NNIPs。StABlE训练迭代执行MD模拟以搜寻不稳定区域，并通过参考可观测量的监督来修正不稳定性。该训练流程借助玻尔兹曼估计器实现，该估计器能高效计算训练神经网络适配系统可观测量所需的梯度，并可检测全局与局部不稳定性。我们在有机分子、四肽及凝聚相系统上，结合三种现代NNIP架构验证了该方法。在全部三个案例中，StABlE训练的模型在模拟稳定性以及结构与动态可观测量的恢复方面均取得显著提升。某些情况下，StABlE训练的模型性能优于基于大50倍数据集的传统训练模型。作为适用于多种NNIP架构与系统的通用框架，StABlE训练是训练稳定且精确NNIPs的强大工具，尤其在缺乏大规模参考数据集时。